Association of Opioid Dose Reduction With Opioid Overdose and Opioid Use Disorder Among Patients Receiving High-Dose, Long-term Opioid Therapy in North Carolina

Key Points Question Is rapid dose decrease or discontinuation among patients receiving high-dose, long-term opioid therapy associated with increased risk of opioid-related harms? Findings In a retrospective cohort study of 19 443 privately insured patients who received high-dose, long-term opioid therapy, rapid dose reduction or discontinuation (vs dose maintenance or increase or gradual reduction or discontinuation) was associated with increased risk of opioid overdose over 4 years of follow-up. Meaning This cohort study found that opioid dose reduction or discontinuation that exceeded current chronic pain management guidelines was associated with increased risk of opioid-related harms, highlighting the importance of caution when reducing opioid doses in order to improve patient safety.


Detailed patient inclusion/exclusion
Patients were excluded if they had a history (using all-available data for lookback 1,2 ) of opioid overdose or OUD (n=1,992), identified using International Classification of Diseases (ICD) version 9, clinical modification (ICD-9-CM) or ICD-10-CM codes in insurance claims. Exposure status could not be assessed prior to the end of the first 30day period; therefore, we excluded 869 patients who were not followed for 30 days past the end of the baseline eligibility period. Time-updated five-digit ZIP-code level characteristics were missing for 22 individuals who were excluded from the analytic cohort.
Patients could reenter the analytic cohort after disenrollment, with follow-up time reset to 0, if they reentered the insurance pool and again met eligibility criteria (n=1,867). The statistical approach accounted for repeated measures and variable follow-up duration.

Calculating Daily MME
First, using dose and quantity indicated in the prescription's National Drug Code and prescription days' supply recorded in the outpatient pharmaceutical claim record, dose per unit and number of units dispensed were multiplied and then divided by prescription days' supply, with the resulting milligrams of medication/day then multiplied by an MME conversion factor from CDC conversion tables for each prescription. 3 When overlapping prescriptions of the same ingredient occurred, we assumed that if the start date was ≤7 days before the end date of the previous prescription, this reflected an early refill and the start date of that prescription was pushed forward to the end date of the previous prescription. 4 Otherwise, the prescriptions were assumed to truly overlap. We allowed for gaps in prescriptions in the baseline period by only requiring 90% days at 90 MMEs. Daily MME for each calendar day was then calculated as the sum of MMEs per day across all prescriptions a patient had on that day.

Defining exposure trajectories
During each 30-day period, we compared average dose during the current month to both (1) average dose from the previous month and (2) a 6-month rolling average to classify patients' prescription trajectories as dose maintained, increased, gradually decreased, rapidly decreased, gradually discontinued, or rapidly discontinued (eTable 2). Specifically, we determined a patient's average dose in the current month and calculated the change in dose from the previous month as well as the change in dose from a 6-month rolling average and used both of these values to determine dose trajectory. We included the comparison with a 6-month rolling average, in addition to accounting for overlapping prescriptions, in order to minimize the impact of short-term dose variabilities on exposure classification. We defined gradual dose reduction following CDC guideline recommendations of ≤10% dose reduction per week (≤34% per month) and anything faster as rapid dose reduction.

Deriving inverse probability weights
We used stabilized IP treatment weights (IPTW) to account for time-dependent confounding. 5 To calculate the IPTW denominator, we used pooled logistic regression to model the probability of experiencing a first rapid dose reduction/discontinuation event during each month of follow-up, given relevant baseline and time-varying confounders. The IPTW denominator included continuous time (restricted cubic spline with 4 knots); all confounders described above; and a one-month lag of other prescriptions, pain (acute or chronic), and surgery. The IPTW numerator was calculated as above, with confounders removed from the model. To account for possible selection bias stemming from potentially informative censoring, we calculated stabilized IP censoring weights (IPCW) in a manner analogous to IPTW, modeling the probability of remaining uncensored given relevant confounders and exposure status. We then multiplied IPCW by IPTW to obtain IPTC-weights.
We examined the weights distribution to ensure a mean of one, and truncated weights at the 0.02 nd and 99.98 th percentiles to minimize the influence of extreme weights. 5 Covariate balance was assessed using standardized mean differences. After weighting, covariates were well balanced. Abbreviations: HDLTOThigh-dose long-term opioid therapy a Some individuals had both a nonfatal and then a fatal overdose, explaining why the number of combined events is less than the number of fatal overdoses plus the number of nonfatal overdoses. b Person-months of follow-up differ across each outcome analysis because an individual may have experienced a nonfatal outcome (e.g., OUD or nonfatal opioid overdose) prior to a fatal overdose. Therefore, that individual would contribute fewer person-months to the analysis with the nonfatal outcome than to the fatal opioid overdose outcome analysis. a referent level is maintained dose, increased dose, or gradually decreased/discontinued dose Abbreviations: IPTCWinverse probability of treatment and censoring-weighted; OUDopioid use disorder; HDLTOThigh-dose long-term opioid therapy